The invention relates generally to the field of gas turbine engine modeling. More specifically, the invention relates to methods and systems that mitigate the effects of input measurement variability on long-term gas turbine engine module performance tracking.
The area of gas turbine performance diagnostics concerns tracking changes in engine module performance measures, such as efficiency and flow parameters, as the engine deteriorates over time. The engine modules that are tracked are typically the compressor and turbine elements of an engine. For example, for a two-spool turbofan engine, the modules would generally be the fan, the low pressure compressor (LPC), the high pressure compressor (HPC), the high pressure turbine (HPT), and the low pressure turbine (LPT). The primary sources of information driving this methodology are operational measurements acquired along an engine's gas path, such as temperatures, pressures, speeds, etc. Tracking fleets of engines across a wide customer/aircraft base offers the added complexity that the measured parameters are affected by different instrumentation calibration and recording fidelity that proves to be non-repeatable across installations.
A successful engine performance diagnostic methodology must include a provision for mitigating measurement non-repeatability. Scatter in measured signal parameters produces anomalies in calculated estimates of engine module performance. Since a performance tracking process must address slow, long-term performance degradation as well as fast, short-term performance degradation (fault anomaly detection), filtering the input data streams is not an acceptable means of reducing dispersion in performance estimates.
One approach to estimating gas turbine engine module performance changes from gas path measurement data uses a linear estimation process modeled after a Kalman filter. The changes are calculated as percent deltas (% Δ) from a known reference or baseline condition, typically a production level engine. Gas path parameter measurements, such as temperatures, pressures, speeds, etc. are corrected and normalized for flight conditions, and compared to the reference baseline to calculate a percent of point changes for each measured parameter. The attendant changes in engine module performance (delta efficiency and flow parameters) are calculated through a series of calculations that involve the calculated percent deltas, known measurement non-repeatabilities, and engine, sensor, and engine system fault influence characteristics.
Engine module performance estimation and tracking has evolved over the last three decades and has produced numerous methods to enable this activity. Some of these methods are model based and make use of linear engine models and Kalman filters for estimating performance changes. Other methods depend entirely on empirical relationships derived from observed engine test and engine flight data. Yet others consist of physics-based methods, empirical methods, and hybrid combinations of the two. The latter methods make use of techniques that include artificial neural networks (ANNs), fuzzy logic, Bayesian belief networks, support vector machines (SVMs), probabilistic neural networks, genetic algorithms, and the like. Combinations of both physics-based model approaches and empirical approaches to form hybrid model methods have also been used.
No matter what method is employed to estimate engine performance changes, variance and noise manifest in the gas path signal data streams produce undesirable characteristics in associated engine module output. In gas turbine monitoring applications, it is not uncommon to have noise present in the input gas path parameter delta input data streams that induce unacceptable scatter in the performance estimate outputs.
The noise that manifests itself in gas path signal measurement delta data streams is due to sensor instrument non-repeatability, reference model errors, flight conditions, ambient variability, altitude (Reynold's) effects, and others. It is also of interest to detect, and subsequently isolate, engine system faults, sensor faults, and anomalous engine operation hidden in rapid measurement parameter delta shifts unlike gradual deterioration that introduces a more gradual overall trend over time. Filtering an input measurement delta parameter data stream (to reduce variability impact on the output) may mask anomalous behavior and make detection of anomalies difficult.
What is needed is an efficient means to reduce estimation non-repeatability and increase measurement fidelity while maintaining unfiltered input data streams that supports early anomaly detection.